Laser-induced Fluorescence Lidar Research Articles

Regional Models for Sentinel-2/MSI Imagery of Chlorophyll a and TSS, Obtained for Oligotrophic Issyk-Kul Lake Using High-Resolution LIF LiDAR Data

The development of regional satellite bio-optical models for natural waters with high temporal and spatial variability, such as inland seas, reservoirs, and coastal ocean waters, requires the implementation of an intermediate measuring link in the chain, “water sampling—bio-optical models”, and this link must have certain intermediate characteristics. The most crucial of them are the high-precision measurements of the main water quality parameters, such as the concentration of chlorophyll a (Chl a), colored dissolved organic matter (CDOM), and total suspended sediments (TSS) in the upper water layer, together with a high operational rate and the ability to cover a large water area in a short time, which corresponds to a satellite overpass. A possible solution is to utilize laser-induced fluorescence (LIF) of water constituents measured by a marine LiDAR in situ with a high sampling rate from a high-speed vessel. This allows obtaining a large ground-truth dataset of the main water quality parameters simultaneously with the satellite overpass within the time interval determined by NASA protocols. This method was successfully applied to the oligotrophic Issyk-Kul Lake in Kyrgyzstan, where we obtained more than 4000 and 1000 matchups for the Chl a and TSS, respectively. New preliminary regional bio-optical models were developed on the basis of a one-day survey and tested for archive Sentinel-2A data for 2022. This approach can be applied for regular monitoring and further correction in accordance with seasonal variability. The obtained results, together with previously published similar studies for eutrophic coastal and productive inland waters, emphasize the applicability of the presented method for the development or adjustment of regional bio-optical models for water bodies of a wide trophic range.

Remote Identification of Oil Films on Water via Laser-Induced Fluorescence LiDAR

Laser-induced fluorescence lidar (LIF-LiDAR) is an effective technology for the stand-off detection, identification, and quantification of oil spills on the water surface. In this work, by using a self-developed LIF-LiDAR system, the laser-induced spectra of different oils with different thicknesses on the water surface were remotely measured at a distance of 100 m. The traditional algorithms based on the similarity degree of matching spectra are adopted to identify the oil types, which results in the 65% (min = 33.68%) and 85% (max = 98.66%) average matching accuracy when the pure oil spectra (infinite thickness) and the average oil spectra (different thickness) are introduced as the database, respectively. Furthermore, to avoid the influence of the pretreatment process in the traditional matching method, the machine learning (ML) model is applied to classify the oil types and an identifying accuracy of nearly 100% is successfully achieved. Our results presented in this work not only demonstrate the good performance of the classification models of fine tree, linear discriminant analysis (LDA), support vector machine (SVM), <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbor (KNN), and neural network (NN) in remote oil-type identification but also the capability of the combination of LIF-LiDAR and ML algorithms on the high-accuracy identification in the types of oil films on water.

A portable UAV-based laser-induced fluorescence lidar system for oil pollution and aquatic environment monitoring

A portable laser-induced fluorescence (LIF) lidar detection system for the unmanned aerial vehicle (UAV) was developed. The system consists of a transmitting and receiving module, a data processing module and a wireless transmission module. The total weight of the LIF lidar system is 5 kg. The system emits 405 nm laser to irradiate the water body, which can realize the monitoring of oil spills and chlorophyll content in the seawater by receiving the fluorescence signal with a spectral range of 400–750 nm. The fluorescence detection data of the LIF system on the UAV are synchronized to the ground terminal through a wireless transmission module, and the acquired fluorescence data are processed by a ground computer in real-time. The performance of the system was tested at a flight altitude of 30 m, and the spectral data of the seawater and oil slicks seawater were studied. The obtained results demonstrate the ability of the LIF lidar system to realize seawater fluorescence remote sensing measurement. The system is convenient and inexpensive for routinely sea monitoring and oil spill monitoring on a regional scale.

Novel Combined Spectral Indices Derived from Hyperspectral and Laser-Induced Fluorescence LiDAR Spectra for Leaf Nitrogen Contents Estimation of Rice

Spectra of reflectance (Sr) and fluorescence (Sf) are significant for crop monitoring and ecological environment research, and can be used to indicate the leaf nitrogen content (LNC) of crops indirectly. The aim of this work is to use the Sr-Sf features obtained with hyperspectral and laser-induced fluorescence LiDAR (HSL, LIFL) systems to construct novel combined spectral indices (NCIH-F) for multi-year rice LNC estimation. The NCIH-F is in a form of FWs* Φ + GSIs* Φ , where Φ is the Sr-Sf features, and FWs and GSIs are the feature weights and global sensitive indices for each characteristic band. In this study, the characteristic bands were chosen in different ways. Firstly, the Sr-Sf characteristics which can be the intensity or derivative variables of spectra in 685 and 740 nm, have been assigned as the Φ value in NCIH-F formula. Simultaneously, the photochemical reflectance index (PRI) formed with 531 and 570 nm was modified based on a variant spectral index, called PRIfraction, with the Sf intensity in 740 nm, and then compared its potential with NCIH-F on LNC estimation. During the above analysis, both NCIH-F and PRIfraction values were utilized to model rice LNC based on the artificial neural networks (ANNs) method. Subsequently, four prior bands were selected, respectively, with high FW and GSI values as the ANNs inputs for rice LNC estimation. Results show that FW- and GSI-based NCIH-F are closely related to rice LNC, and the performance of previous spectral indices used for LNC estimation can be greatly improved by multiplying their FWs and GSIs. Thus, it can be included that the FW- and GSI-based NCIH-F constitutes an efficient and reliable constructed form combining HSL (Sr) and LIFL (Sf) data together for rice LNC estimation.

Analyzing the effect of the incidence angle on chlorophyll fluorescence intensity based on laser-induced fluorescence lidar.

Laser-induced fluorescence (LIF) technology has been widely applied to monitor vegetation growth status and biochemical concentrations. Thus, it is important to accurately acquire the fluorescence information for the quantitative monitoring of vegetation growth status. In this study, firstly, the incidence angle's effect on chlorophyll fluorescence intensity was analyzed by using the FluorMODleaf model. Then, comprehensive experimental data on the angle dependence of the fluorescence intensity to vegetation leaf surface were collected. Numerical and experimental results showed that proposed corrected cosine expression could be used to describe the relationship between the incidence angle and the fluorescence intensity in the LIF-Lidar. Lastly, fluorescence signals at 685 and 740 nm extracted at different incident angles of excitation lights were fitted with the corrected cosine expression. The coefficient of determination (R2) of the fitting results reached a maximum value of 0.93 for Salix babylonica.

Multiwavelength laser induced fluorescence (LIF) LIDAR system for remote detection and identification of oil spills

A multiwavelength laser induced fluorescence (LIF) LIDAR system, which can be employed around the clock for detecting oil spills in a marine environment, has been developed. The system incorporates a pulsed Nd: YAG laser, Nd: YAG laser amplifier and two KD*P crystals for generating 266 nm, 355 nm and 532 nm. Additionally, the LIF LIDAR system incorporates a nitrogen laser pumped dye laser system operating at 428 nm (Stilbene 420 dye), a Newtonian telescope, motorized monochromator, a photomultiplier detection system and a data acquisition system. Fluorescence intensity measurements collected from fresh and weathered oil samples using the LIF LIDAR system are presented. Strong fluorescence spectra were detected from refined and crude oils when excited with 266 nm. Finally, a brief description for the principle components algorithm used for identifying and classifying unknown oil samples is provided.

Preliminary measurements of fluorescent aerosol number concentrations using a laser-induced fluorescence lidar.

A laser-induced fluorescence lidar has been developed for detecting the concentration of fluorescent aerosols in the air. The fluorescence lidar was constructed with a pulsed fourth-harmonic Nd:YAG laser at the ultraviolet wavelength of 266nm with a repetition rate of 10Hz. A 250mm diameter custom telescope was used to collect optical spectra ranging from 260 to 560nm. Fluorescence signals with wavelengths ranging from 310-440nm were extracted, exploring a filter with a bandwidth of 130nm. The preliminary experiments were conducted at the campus of Xi'an University of Technology, in which the fluorescence signals of atmospheric fluorescent aerosols were continuously collected from 20:00 to 23:00 CST on 13 December 2017. Based on the fluorescence lidar equation, the density of fluorescence signals was calibrated using Rayleigh-Mie scattering signals and ozone (O3) concentration data at the ground level. Measured ranges show a strong dependence with the O3 concentrations due to its absorption characteristics at ultraviolet 266nm. Moreover, the concentration of the biogenic particles was also calculated based on the raw data of the fluorescence channel. Obtained results show that the concentration of biogenic particles in the Xi'an area varied greatly, ranging from 3456 particles · m-3 to 8835 particles · m-3 during winter.

Application of the chlorophyll fluorescence ratio in evaluation of paddy rice nitrogen status

In this research, laser-induced fluorescence (LIF) technique combined with back-propagation neural network (BPNN) was employed to analyse different nitrogen (N) fertilization levels in paddy rice. Leaf fluorescence characteristics (FLCs) were measured by using the LIF system built in our laboratory and exhibited different FLCs with different nitrogen fertilization levels. The correlation between fluorescence intensity ratios (F685/F460, F735/F460 and F735/F685) and the dose of N fertilization was established and analysed. Then, the BPNN algorithm was utilized to validate that the different N fertilization levels can be classified based on the three FLCs. The overall identification accuracies of 2014 and 2015 were 90% and 92.5%, respectively. Experimental results demonstrated that the three FLCs with the help of multivariate analysis can be served as a helpful tool in the evaluation of paddy rice N fertilization levels. Besides, this study can also provide guidance for the selection of LIF Lidar channels in the following research.

Ultraviolet laser-induced fluorescence lidar for pollen detection

Pollen is one of the important allergens in allergic diseases. The objective of this research effort is to investigate the use of a fluorescence lidar to measure concentrations of pollens in the atmosphere. The fluorescence lidar was constructed with a pulsed Nd:YAG laser, employing at fourth harmonic and second harmonic. A 250mm diameter custom telescope was used to receive optical spectra from 260 nm–560nm excited by 266nm, and R7506 Photomultiplier tubes for two linear orthogonal polarization applications at 532nm. Range-resolved fluorescence signals are collected in 32 channels of compound PMT sensor coupled with Czerny–Turner spectrograph. Based on the current configurations, we performed a series of simulations to estimate the measurement range and the concentration resolutions of pollens. With a relative error of less than 10%, theoretical analysis shows that the system is capable of identify a minimum concentration of pollen grains at 19 particlesL−1 and 1 particlesL−1 at daytime and nighttime within a range of 1.0km, which is capable of identify a minimum concentration of pollen grains at 903 particlesL−1 and 10 particlesL−1 at daytime and nighttime within a range of 5.0km.

Effect of fluorescence characteristics and different algorithms on the estimation of leaf nitrogen content based on laser-induced fluorescence lidar in paddy rice.

Paddy rice is one of the most significant food sources and an important part of the ecosystem. Thus, accurate monitoring of paddy rice growth is highly necessary. Leaf nitrogen content (LNC) serves as a crucial indicator of growth status of paddy rice and determines the dose of nitrogen (N) fertilizer to be used. This study aims to compare the predictive ability of the fluorescence spectra excited by different excitation wavelengths (EWs) combined with traditional multivariate analysis algorithms, such as principal component analysis (PCA), back-propagation neural network (BPNN), and support vector machine (SVM), for estimating paddy rice LNC from the leaf level with three different fluorescence characteristics as input variables. Then, six estimation models were proposed. Compared with the five other models, PCA-BPNN was the most suitable model for the estimation of LNC by improving R2 and reducing RMSE and RE. For 355, 460 and 556 nm EWs, R2 was 0.89, 0.80 and 0.88, respectively. Experimental results demonstrated that the fluorescence spectra excited by 355 and 556 nm EWs were superior to those excited by 460 nm for the estimation of LNC with different models. BPNN algorithm combined with PCA may provide a helpful exploratory and predictive tool for fluorescence spectra excited by appropriate EW based on practical application requirements for monitoring the N status of crops.

Simulation of laser induced fluorescence lidar detecting system

Simulation of laser induced fluorescence lidar detecting system

Excitation Wavelength Analysis of Laser-Induced Fluorescence LiDAR for Identifying Plant Species

Laser-induced fluorescence (LIF) is an active technology that is closely related to excitation wavelength (EW). This study has mainly analyzed the performance of LIF LiDAR with different EWs in distinguishing plant species. The 355-, 460-, and 556-nm lasers were utilized to excite leaf fluorescence. The fluorescence signals were measured by the LIF system built in the laboratory. Subsequently, principal component analysis combined with back-propagation neural network was used to analyze fluorescence spectra. For the three EWs, the overall identification rates of the eight plant species were 75%, 80%, and 87.5%. However, when the plant species of the same genus were taken as a category, the overall classification rates were 92.5%, 81.3%, and 86.3%. Experimental results demonstrated that, when the plant species of the same genus were regarded as a category, 355 nm was the optimal EW. However, 556 nm was superior to 355 and 460 nm in the identification of plant species of the same genus.

THE EFFECTIVE OF DIFFERENT EXCITATION WAVELENGTHS ON THE IDENTIFICATION OF PLANT SPECIES BASED ON FLUORESCENCE LIDAR

Laser-induced fluorescence (LIF) served as an active technology has been widely used in many field, and it is closely related to excitation wavelength (EW). The objective of this investigation is to discuss the performance of different EWs of LIF LiDAR in identifying plant species. In this study, the 355, 460 and 556 nm lasers were utilized to excite the leaf fluorescence and the fluorescence spectra were measured by using the LIF LiDAR system built in the laboratory. Subsequently, the principal component analysis (PCA) with the help of support vector machine (SVM) was utilized to analyse fluorescence spectra. For the three EWs, the overall identification rates of the six plant species were 80 %, 83.3 % and 90 %. Experimental results demonstrated that 556 nm excitation light source is superior to 355 and 460 nm for the classification of the plant species for the same genus in this study. Thus, an appropriate excitation wavelength should be considered when the LIF LiDAR was utilized in the field of remote sensing based on the LIF technology.

THE EFFECTIVE OF DIFFERENT EXCITATION WAVELENGTHS ON THE IDENTIFICATION OF PLANT SPECIES BASED ON FLUORESCENCE LIDAR

Abstract. Laser-induced fluorescence (LIF) served as an active technology has been widely used in many field, and it is closely related to excitation wavelength (EW). The objective of this investigation is to discuss the performance of different EWs of LIF LiDAR in identifying plant species. In this study, the 355, 460 and 556 nm lasers were utilized to excite the leaf fluorescence and the fluorescence spectra were measured by using the LIF LiDAR system built in the laboratory. Subsequently, the principal component analysis (PCA) with the help of support vector machine (SVM) was utilized to analyse fluorescence spectra. For the three EWs, the overall identification rates of the six plant species were 80 %, 83.3 % and 90 %. Experimental results demonstrated that 556 nm excitation light source is superior to 355 and 460 nm for the classification of the plant species for the same genus in this study. Thus, an appropriate excitation wavelength should be considered when the LIF LiDAR was utilized in the field of remote sensing based on the LIF technology.

Research and analysis on lidar performance with intrinsic fluorescence biological aerosol measurements

Biological aerosols which could cause diseases of human beings, animals and plants, are living particles suspended in the atmosphere. Ultraviolet laser induced fluorescence has been developed as a standard technique used to discriminate between biological and non-biological particles. As an effective tool of remote sensing, fluorescence lidar is capable of detecting concentration of biological aerosols with high spatial and temporal resolutions. Intrinsic fluorescence, one of the most important characteristics of biological aerosols, has quite a large effect on the performances of fluorescence lidar. To investigate the effects of intrinsic fluorescence on biological aerosols, we design an ultraviolet laser induced fluorescence lidar at an excited wavelength of 266 nm, with a repetition rate of 10 Hz. Fluorescence signals are collected by a Cassegrain telescope with a diameter of 254 mm, in which fluorescence spectra of 300-800 nm are mainly considered. A spectrograph and a multichannel photomultiplier tube (PMT) array detector are employed to achieve the fine separation and highefficiency detection of fluorescence signals. According to the present configuration, we perform a series of simulations to estimate the measurement range and the concentration resolution of biological aerosols, with a certain pulse energy. With a relative error less than 10%, theoretical analysis shows that designed fluorescence lidar is able to detect the biological aerosols within a range of 1.5 km at a concentration of 1000 particles·L-1. When the detection distance enlarges to 2.1 km, detectable wavelength range is limited to 300-310 nm. In addition, the lidar is capable of identifying minimum concentrations of biological aerosols with 2 particles·L-1 and 4 particles·L-1 at fluorescence wavelengths of 350 nm and 600 nm, respectively, where the induced pulse energy is set to be 60 mJ and detected range 0.1 km. With setting energies of 40 mJ and 20 mJ, minimum concentrations of biological aerosols decrease to 3 particles·L-1 and 6 particles·L-1, respectively, at a fluorescence wavelength of 350 nm. The relative error of minimum concentration resolution is about 2 particles·L-1, increasing rapidly with range. For a fluorescence wavelength of 600 nm, both the minimum concentration and the relative error show relatively high values, 5 particles·L-1 at 40 mJ and 10 particles·L-1 at 20 mJ, where the relative errors are found to be 2 particles·L-1 and 4 particles·L-1, respectively. The results prove that a shorter intrinsic fluorescence wavelength has a better effect on biological aerosol measurement. We believe that a proper intrinsic fluorescence wavelength will further improve the detection accuracy of biological aerosols.

Development of a UV laser-induced fluorescence lidar for monitoring blue-green algae in Lake Suwa.

We developed a UV (355nm) laser-induced fluorescence (LIF) lidar for monitoring the real-time status of blue-green algae. Since the fluorescence spectrum of blue-green algae excited by 355nm showed the specific fluorescence at 650nm, the lidar was designed to be able to detect the 650nm fluorescence as a surveillance method for the algae. The usefulness was confirmed by observation at Lake Suwa over four years (2005-2008). The detection limit of the LIF lidar was 16.65mg/L for the blue-green algae, which is the range of concentrations in the safe level set by the World Health Organization.

Pollution detection using the spectral fluorescent signatures (SFS) technique

Pollution detection using the spectral fluorescent signatures (SFS) technique

Fluorescence lidar for remote monitoring of plant

The laser-induced fluorescence (LIF) characteristic of plant is directly linked to the photosynthesis. The LIF lidar for remote monitoring of plant has been suggested as one of the useful tools to identify plant species and determine its physiological status for a long time. So recently a LIF lidar for remote sensing of plant in Anhui Institute of Optics and Fine Mechanics is developed. It transmits laser beam at wavelength of 354.7 and 532 nm, and receives elastic echo and fluorescence echo at wavelength of 680 and 740 nm. Numerical simulations are carried out to determine achievable lidar performance including operation range. Validity of fluorescence signal is certified and then some results are presented. Comparison of the fluorescence characteristic among birch, conifer, and algae show that the uorescence lidar is one of the potential tools to differentiate plant species.